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  • Review & Rebuttal ...

    Dear Biomch-L readers,

    Review and rebuttal belong to the essences of academic life. While most of
    this occurs in the privacy of the submission and acceptance process for refe-
    reed (archive) journals such as the Journal of Biomechanics, the debate may
    become a public one (once a paper has been published) by counterpublications
    or letter(s) to the Editor. Of course, if a paper is published without an
    extensive review procedure, one may either leave the matter as it is, or opt
    for a public response. In the present case, the latter seems justified, as
    a number of readers on this list have been using some of the published files
    on the Biomch-L file server.

    During last week's 1st European Conference of Biomedical Engineering, I was
    pleased to hear a reference to my own work on natural spline smoothing and
    differentiation (see the relevant package which can be retrieved via the
    request SEND GCVSPL FORTRAN to LISTSERV@HEARN -- subscribers only). In their
    paper "A Procedure for Quantitative Comparison of Movement Data", co-authored
    by Moreno D'Amico, Giancarlo Ferrigno, and Giorgio C. Santambrogio from the
    Centro di Bioingegneria, Dipartimento di Bioengignerio, Politecnico di Milano,
    Fondazione Pro Juventute in Milan, Italy, a general procedure encompassing
    data acquisition, pre-treatment, check of steady-state, normalisation,
    stratification, collection, statistic testing, and I.E.D. computation is
    provided, with cited references to the authors' own work only. Smoothing
    and derivative estimation were covered in a few lines under "pre-treatment".

    During the oral presentation, however, the first author confined himself to
    a comparison between his own smoothing and differentiation procedure (an AR
    model with automatic order determination) and the GCVSPL package, claiming
    that the GCVSPL package was generally viewed as the best in the litterature.
    (While this was a very flattering statement, I have never come across such a
    claim.) More seriously, some of the visitors to this lecture were annoyed
    that the spoken presentation covered only a small part of the published
    abstract. (There were 5 parallel sessions, and one had to shuttle between
    sessions on the basis of the conference Proceedings. When a subsequent
    speaker proved to be a no-show, the paper was debated at length, but no
    further details could be obtained on the remainder of the published abstract.)

    The speaker proceeded by comparing the results of his package with those from
    GCVSPL when operated in quintic spline mode, using 3rd derivatives obtained
    with either package. While third derivatives have, indeed, been assessed with
    a natural, quintic spline precursor to the GCVSPL package (see my Human Movement
    Science 1985 paper), this was done to demonstrate that this is not a proper
    procedure. Use of natural splines imposes zero end conditions from certain
    derivatives and up. This is fine if the data meet such a condition sufficient-
    ly; in the opposite case, serious artefacs may occur t h r o u g h o u t the
    data record, and not just at the record end(s) as suggested by, e.g., Hatze in
    his 1981/1 J. of Biomechanics paper.

    For these reasons, it was recommended in the 1986 ESB Berlin Proceedings (not
    in the 1986 Advances in Engineering Software paper describing the GCVSPL
    package, as I said during the debate at the Nice meeting) that the half spline
    order should be higher than the highest derivative sought. This leads to the
    following table:

    Spline order Spline name Derivative
    2 Linear Smoothed data only
    4 Cubic Up to and including 1st Derivative
    6 Quintic ,, ,, ,, ,, 2nd Derivative
    8 Heptic ,, ,, ,, ,, 3rd Derivative

    (estimating 3rd and higher derivatives is, however, a highly ill-posed problem
    and may fail unless the data are very accurate).

    In fact, the smoothing constraint occurs in the same, lowest derivative that
    imposes zero-end boundary conditions in the case of natural splines. Thus,
    even if no zero boundary constraints are imposed (as when using periodic or
    complete splines), the same table as above should be used in order to avoid
    a negative bias in the highest derivative sought. While one might use higher-
    order splines than advocated above, this has the disadvantage of a steeper
    transition bandwidth in the equivalent Butterworth filter: see the paper in
    the 1986 ESB Berlin Proceedings.

    Given the above choice of the spline order, GCVSPL allows a variety of options
    to obtain `optimal' results. Some of these options require iterative operation
    of the package, and this may naturally be time-consuming. My Italian colleague
    provided some timing comparisons, and concluded that his package is much faster
    than the spline package. However, he did not state whether this was done in the
    MODE < 0 mode (where previously assessed matrices are reused). Using GCVSPL
    with the provided test data, such differences can be substantial. Furthermore,
    I should add that I have never bothered to look at great length into computatio-
    nal speed ; one may easily experiment with this, by changing the TOL parameter
    in the GCVSPL subroutine (currently 1D-6) into a larger value (e.g., 1D-3).
    With a little experimenting to-day, I found no serious differences in the
    quality of the estimation procedure, while timing improvements on the order
    of 30% could be reached. However, timing is of limited importance if (as in
    video-based movement analysis) other parts of the data processing chain require
    much time, too.

    Furthermore, the speaker claimed that his (third) derivatives were less noisy
    than the spline package; however, I do not recall seeing any comparison between
    known, true derivatives and estimated ones with either procedure. At any rate,
    it may be useful to experiment with initial guesses for the smoothing parameter,
    by first running GCVSPL in MODE=1 with a high smoothing parameter (or in MODE=4
    with a large number of degrees of freedom), and than run the package in MODE=-2.
    This will force the package to start searching for the optimal amount of smooth-
    ing from a low, equivalent cut-off frequency.

    The frequency-domain explanation is as follows. Assuming a signal + noise power
    spectrum as depicted below,

    | xxxx
    | xxx x
    | xx x x
    | xxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx
    0 freqency ---> Fnyquist

    the high-frequency peak may be `signal' or `noise'; neither algorithm knows
    this a priori. If we have reason to believe that the high peak is a true
    signal component, it should clearly be taken along; in the opposite case
    (e.g., mains hum at 50, 60, 100, or 120 Hz), it should clearly be discarded.

    An iterative procedure such as GCVSPL may discard the peak if searching from
    below unless it is too strong, and accept it unconditionally if searching from
    above. N.B.: If the noisy data truly represent a low-pass signal with `white',
    additive, uncorrelated noise (i.e., with a flat spectrum), the highest frequency
    is choosen where the data spectrum becomes essentially flat. Note, however,
    that this is only true if there is sufficient redundancy in the data, i.e., a
    significant high-frequency part of the spectrum is, indeed, flat: the criteria
    via which both procedures estimate an optimum choice are based on noisy data,
    and are, therefore, noisy stochastics themselves requiring sufficient

    One should note also that finding an optimal amount of smoothing on the
    original data level does not necessarily imply that also the derivatives of
    this optimally estimated signal are in some way optimal. As apparent from
    the Berlin 1986 paper (see also the paper "Effects of Data Smoothing on the
    Reconstruction of Helical Axis Parameters in Human Joint Kinematics" by De
    Lange et al., J. of Biomech. Engng 112, May 1990, 107-113, Fig. 4), more
    smoothing can, in fact, be appropriate. Actually, this finding may appease
    the fear of some users of automatic cut-off determining algorithms that
    unwarranted oversmoothing is performed; at the same time, the results
    obtained f o r t h e g i v e n d a t a were quite satisfactory.
    Hatze's 1981 paper justifiably addressed the issue of optimality between

    The morale of this (possibly unusual) rebuttal is a simple one: never trust an
    automatic algorithm's results blindly. Try to understand what the algorithm
    does, and play around with it until you know its pro's and con's. For the
    GCVSPL package, a wise activity is to run it for a given set of data at a
    logarithmically increasing series of smoothing parameters in MODE=-1 (first
    time +1), and plot the residual variance and GCV function values. This will
    provide an idea of any multiple optima caused by high-frequency peaks such as
    the one depicted above.

    Reference: M. D'Amico & G. Ferrigno, Techniques for the Evaluation of Deriva-
    tives from Noisy Biomechanical Displacement Data Using a Model-Based Bandwidth-
    Selection Procedure. Med. & Biolog. Engng & Comp. 28(1990), 407-415.

    P.S. Since the Italian group is not, to my knowledge, subscribing to Biomch-L,
    I'll send a copy of this posting to them by facsimile. Hopefully, they will
    respond on the list. Regretfully, I may not be accessible for a while by
    email as of the first of March, but I'll keep in touch with this list somehow.

    Herman J. Woltring
    Brussellaan 29
    The Netherlands
    Tel. (private) +31.40.480869
    Voice/fax/modem +31.40.413744